資料抽取與文件

typesafe-computer-use 根據文字目標操作 Mac，透過 OCR 與輔助使用控制項讀取桌面。 TypeSafe Jev 選擇動作、目標與網站；程式碼提供事實，獨立的寫作模型負責自由文字。 需要 TypeSafe 金鑰、螢幕錄製及輔助使用權限。命令列支援先預演，再實際控制滑鼠與鍵盤。

JevChat-Windows 透過本地 OCR 讀取微信與 KakaoTalk 對話，在聊天視窗旁提供候選回覆。 Jev 判斷意圖、緊張程度並為三則候選排序；獨立語言模型負責起草。 在 Windows 10 1903+ 或 Windows 11 上，分別設定判斷服務與起草服務的金鑰。

此函式庫利用表單描述 JSON 名錄，從擷取的文字辨識 IRS 表單與頁面類型。 將文字行、jevBackend 及 criteria.json 傳給 classifyPage。PDF 輔助功能另需 Poppler，後端需要 TypeSafe 金鑰。

DocJev 可分類 PDF、DOCX 和 PPTX，或將合併文件拆成帶類別的頁碼範圍，並可匯出 PDF。 以 uv 安裝 Python 套件，匯出 TYPESAFE_API_KEY，再提供分類規則執行 docjev classify 或 split。Office 格式需要 LibreOffice。

工具擷取文件內容並在 Qdrant 建立索引，規劃大綱後依本機資料逐節研究。 Jev 判斷並重排檢索片段、評估證據缺口；另由語言模型規劃、查詢及寫作，Python 控制重試。

Data extraction for systematic reviews, quoted from the papers. Ask a trial report and its supplements your extraction form or a RoB 2, ROBINS-I, QUADAS-2 or TIDieR template; Jev points at the lines, every answer is a verbatim quote with its page, you check it and export the table. Files stay in your browser.

A Document OCR Router to help route pages based on content.

Open-source macOS AI computer use and native GUI automation on Apple silicon. Jev + OmniParser CoreML + Apple Vision OCR. Bring your own OpenRouter, Vercel AI Gateway, or TypesafeAI token.

Use Jev (TypeSafe's System One model) as a calibrated reranker: one call, up to 30 documents, a probability per document. Apache-2.0.

Route form submissions to the right people with Jev and AI SDK.

Windows 微信回复助手（非官方修改版）：本地离线 OCR 读屏 + 模型起草候选回复 + 一键填入微信输入框，可选自动发送。不 hook、不注入、不读微信数据库。

DeepSeek + TypeSafe JEV 微信回复助手：Windows 正式版、Apple Silicon macOS 预览版；本地 OCR 与人工可控回复

JEV Document Classification enables the rapid and cost-effective classification of text-based documents using AI, leveraging TypeSafe's "System One" model.

DeepSeek Harness (dsh) 插件:论文伴读工作台 —— PDF 转录 / 检索 / 流式问答 + 内置阅读器页面。

Semantic code and document search CLI — BM25 recall + TypeSafe Jev calibrated precision

Ask a PDF in your own words and watch the matching lines light up. React + pdf.js + TypeSafe Jev.

Local-first document evaluation workspaces powered by Jev

.NET client for TypeSafe System One (Jev) — typed questions in, typed answers with probabilities and confidence out. No prompt engineering, no output parsing.

Natural-language MCP tool dispatcher powered entirely by TypeSafe's Jev — no general-purpose LLM. Discovers a simple MCP server's tool signatures at runtime and uses Jev's typed primitives (Choice/Noul) to pick the right tool and extract its arguments straight out of the sentence.

A browser form for building requests to TypeSafe's Jev: pick a template, fill in the blanks, copy the request. No JSON, no install, runs locally.

An open-source, AI-powered resume builder designed to craft high-impact resumes. Features smart content enhancement using the STAR method, instant PDF previews, and seamless export. Built with Django, Htmx, OpenAI, and WeasyPrint.

MCP-first toolkit for TypeSafe/Jev — the System One decision model. One stdio server (jev mcp) serves any MCP-capable harness, backed by one local event log and Prometheus impact metrics you can scrape into your own Grafana.

Parsing the PDF and extracting the relevant information

Text/PDF - File categorization and sorting with Typesafe AI Jev or local calibrated decision model